biostudies-literatureChristensen ENatural Sciences and Engineering Research Council of CanadaOntario Institute for Cancer ResearchGenome Canada and Ontario Genomicsbbac561https://www.ebi.ac.uk/biostudies/studies/S-EPMC9851326biostudies-literatureUnknown24(1)Single-cell RNA sequencing (scRNA-seq) clustering and labelling methods are used to determine precise cellular composition of tissue samples. Automated labelling methods rely on either unsupervised, cluster-based approaches or supervised, cell-based approaches to identify cell types. The high complexity of cancer poses a unique challenge, as tumor microenvironments are often composed of diverse cell subpopulations with unique functional effects that may lead to disease progression, metastasis and treatment resistance. Here, we assess 17 cell-based and 9 cluster-based scRNA-seq labelling algorithms using 8 cancer datasets, providing a comprehensive large-scale assessment of such methods in a cancer-specific context. Using several performance metrics, we show that cell-based methods generallBriefings in bioinformaticsEvaluation of single-cell RNAseq labelling algorithms using cancer datasets.PMC9851326R-20-303OGI-167DGECR-2021-00298Husic MNaidas ABrudno MLuo PMahalanabis ARamani AChristensen EDiaz-Mejia JJTurinsky AShooshtari PPugh TfalseEvaluation of single-cell RNAseq labelling algorithms using cancer datasets.Single-cell RNA sequencing (scRNA-seq) clustering and labelling methods are used to determine precise cellular composition of tissue samples. Automated labelling methods rely on either unsupervised, cluster-based approaches or supervised, cell-based approaches to identify cell types. The high complexity of cancer poses a unique challenge, as tumor microenvironments are often composed of diverse cell subpopulations with unique functional effects that may lead to disease progression, metastasis and treatment resistance. Here, we assess 17 cell-based and 9 cluster-based scRNA-seq labelling algorithms using 8 cancer datasets, providing a comprehensive large-scale assessment of such methods in a cancer-specific context. Using several performance metrics, we show that cell-based methods generall2023-01-01T00:00:00Z2023 Jan2026-04-08T13:24:37.703Z2025-02-19T04:45:38.863ZS-EPMC98513263658578410.1093/bib/bbac561